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Erschienen in: Japanese Journal of Radiology 11/2020

01.07.2020 | Original Article

Feasibility of new fat suppression for breast MRI using pix2pix

verfasst von: Mio Mori, Tomoyuki Fujioka, Leona Katsuta, Yuka Kikuchi, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume, Kazunori Kubota, Ukihide Tateishi

Erschienen in: Japanese Journal of Radiology | Ausgabe 11/2020

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Abstract

Purpose

To generate and evaluate fat-saturated T1-weighted (FST1W) image synthesis of breast magnetic resonance imaging (MRI) using pix2pix.

Materials and methods

We collected pairs of noncontrast-enhanced T1-weighted an FST1W images of breast MRI for training data (2112 pairs from 15 patients), validation data (428 pairs from three patients), and test data (90 pairs from 30 patients). From the original images, 90 synthetic images were generated with 50, 100, and 200 epochs using pix2pix. Two breast radiologists evaluated the synthetic images (from 1 = excellent to 5 = very poor) for quality of fat suppression, anatomic structures, artifacts, etc. The average score was analyzed for each epoch and breast density.

Results

The synthetic images were scored from 2.95 to 3.60; the best was reduction in artifacts when using 100 epochs. The average overall quality scores for fat suppression were 3.63 at 50 epochs, 3.24 at 100 epochs, and 3.12 at 200 epochs. In the analysis for breast density, each score was significantly better for nondense breasts than for dense breasts; the average score was 2.88–3.18 for nondense breasts and 3.03–3.42 for dense breasts (P = 0.000–0.042).

Conclusion

Pix2pix had the potential to generate FST1W synthesis for breast MRI.
Literatur
1.
Zurück zum Zitat American college of radiology breast imaging reporting and data system (BI-RADS), vol 2013, 5th edn. American College of Radiology, Reston, VA American college of radiology breast imaging reporting and data system (BI-RADS), vol 2013, 5th edn. American College of Radiology, Reston, VA
2.
Zurück zum Zitat Shin K, Phalak K, Hamame A, Whitman GJ. Interpretation of breast MRI utilizing the BI-RADS Fifth Edition Lexicon: how are we doing and where are we headed? Curr Probl Diagn Radiol. 2017;46:26–34.CrossRef Shin K, Phalak K, Hamame A, Whitman GJ. Interpretation of breast MRI utilizing the BI-RADS Fifth Edition Lexicon: how are we doing and where are we headed? Curr Probl Diagn Radiol. 2017;46:26–34.CrossRef
3.
4.
Zurück zum Zitat Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 2007;244:356–78.CrossRef Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 2007;244:356–78.CrossRef
5.
Zurück zum Zitat Clauser P, Pinker K, Helbich TH, Kapetas P, Bernathova M, Baltzer PAT. Fat saturation in dynamic breast MRI at 3 Tesla: is the dixon technique superior to spectral fat saturation? A visual grading characteristics study. Eur Radiol. 2014;24:2213–9.CrossRefPubMed Clauser P, Pinker K, Helbich TH, Kapetas P, Bernathova M, Baltzer PAT. Fat saturation in dynamic breast MRI at 3 Tesla: is the dixon technique superior to spectral fat saturation? A visual grading characteristics study. Eur Radiol. 2014;24:2213–9.CrossRefPubMed
6.
Zurück zum Zitat Le-Petross H, Kundra V, Szklaruk J, Wei W, Hortobagyi GN, Ma J. Fast three-dimensional dual echo Dixon technique improves fat suppression in breast MRI. J Magn Reson Imaging. 2010;31:889–94.CrossRefPubMed Le-Petross H, Kundra V, Szklaruk J, Wei W, Hortobagyi GN, Ma J. Fast three-dimensional dual echo Dixon technique improves fat suppression in breast MRI. J Magn Reson Imaging. 2010;31:889–94.CrossRefPubMed
7.
Zurück zum Zitat Dogan BE, Ma J, Hwang K, Liu P, Yang WT. T1-weighted 3D dynamic contrast-enhanced MRI of the breast using a dual-echo Dixon technique at 3 T. J Magn Reson Imaging. 2011;34:842–51.CrossRefPubMed Dogan BE, Ma J, Hwang K, Liu P, Yang WT. T1-weighted 3D dynamic contrast-enhanced MRI of the breast using a dual-echo Dixon technique at 3 T. J Magn Reson Imaging. 2011;34:842–51.CrossRefPubMed
8.
Zurück zum Zitat Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal. 2019;58:101552.CrossRefPubMed Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal. 2019;58:101552.CrossRefPubMed
9.
Zurück zum Zitat Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE conference on computer vision and pattern recognition Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE conference on computer vision and pattern recognition
11.
Zurück zum Zitat van der Velden TA, Luijten PR, Klomp DWJ. Improved fat suppression of the breast using discretized frequency shimming. Magn Reson Med. 2018;79:593–9.CrossRefPubMed van der Velden TA, Luijten PR, Klomp DWJ. Improved fat suppression of the breast using discretized frequency shimming. Magn Reson Med. 2018;79:593–9.CrossRefPubMed
12.
Zurück zum Zitat Guan S, Loew M. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J Med Imaging (Bellingham). 2019;6:031411. Guan S, Loew M. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J Med Imaging (Bellingham). 2019;6:031411.
13.
Zurück zum Zitat Kim KH, Do WJ, Park SH. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Med Phys. 2018;45:3120–31.CrossRefPubMed Kim KH, Do WJ, Park SH. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Med Phys. 2018;45:3120–31.CrossRefPubMed
14.
Zurück zum Zitat Quan TM, Nguyen-Duc T, Jeong WK. Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging. 2018;37:1488–97.CrossRefPubMed Quan TM, Nguyen-Duc T, Jeong WK. Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging. 2018;37:1488–97.CrossRefPubMed
15.
Zurück zum Zitat Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550–62.CrossRefPubMedPubMedCentral Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550–62.CrossRefPubMedPubMedCentral
16.
Zurück zum Zitat Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging. 2017;36:2536–45.CrossRefPubMed Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging. 2017;36:2536–45.CrossRefPubMed
17.
Zurück zum Zitat Tetko IV, Livingstone DJ, Luik AI. Neural network studies. 1. Comparison of overfitting and overtraining. J chem Inf Comput Sci. 1995;35:826–33.CrossRef Tetko IV, Livingstone DJ, Luik AI. Neural network studies. 1. Comparison of overfitting and overtraining. J chem Inf Comput Sci. 1995;35:826–33.CrossRef
Metadaten
Titel
Feasibility of new fat suppression for breast MRI using pix2pix
verfasst von
Mio Mori
Tomoyuki Fujioka
Leona Katsuta
Yuka Kikuchi
Goshi Oda
Tsuyoshi Nakagawa
Yoshio Kitazume
Kazunori Kubota
Ukihide Tateishi
Publikationsdatum
01.07.2020
Verlag
Springer Japan
Erschienen in
Japanese Journal of Radiology / Ausgabe 11/2020
Print ISSN: 1867-1071
Elektronische ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-020-01012-5

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